A method to develop a data-driven model able to estimate hydraulic properties based on groundwater level (GWL) fluctuation patterns is proposed. In particular, a preprocessing method using a denoising autoencoder (DAE) is incorporated into the proposed method to improve the performance of the developed method. DAE is applied to prepare the input variable of the estimation model by extracting informative low-dimensional features from the original high-dimensional GWL data. Before applying the proposed DAE to this study, the reliability of applying the DAE is validated. First, the ability to reduce the noise of GWL data is validated by observing that an average of 71% of the noise is reduced. Additionally, the performances of the extracted principal characteristics of the GWL data is confirmed by reasonable matches in the extracted features to the corresponding hydraulics of the aquifers. In this case, both synthetic data and actual data acquired over South Korea are applied. Based on the validated DAE results, models to estimate two types of hydraulic properties are constructed. The estimation performances of the models are quantitatively validated using the correlation coefficient between the estimated and actual hydraulic properties. Overall, the constructed models for k and α/n show an appropriate estimation accuracy with a high correlation coefficient between the actual result and estimate (0.8663 and 0.7207, respectively). Therefore, using the proposed method, the hydraulic properties of an un-informed aquifer can be effectively inferred given GWL data without conducting field experiments (e.g., pumping tests). The proposed method is promising for efficient evaluations of the physical hydraulics of un-informed aquifers and, therefore, can be used as an effective tool to manage groundwater resources.
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 202008980000 ) and by Korea Environment Industry & Technology Institute ( KEITI ) through Water Supply Service Program corresponding to Groundwater Requirement, funded by Korea Ministry of Environment (MOE). The associated codes and the data used in this study are available upon request from the corresponding author Jina Jeong (contact email: email@example.com ).
All Science Journal Classification (ASJC) codes
- Water Science and Technology